Modeling of Piezoelectric Actuators Based on Bayesian Regularization Back Propagation Neural Network

摘要

Piezoelectric actuators are the key components in micro/nano manufacturing. But the hysteresis nonlinearity seriously affects working performance of actuators. So a lot of models were proposed to describe the hysteresis nonlinearity. A popular model which was widely used is the Preisach model. In order to obtain accurate displacement output corresponding to arbitrary input voltage with the Preisach model, function output approximation is needed. In this paper, firstly the Preisach model was introduced. Then the function modeling of Preisach model based on a Bayesian Regularization Back Propagation Network (BRBPN) was presented, and a three layers BPN was designed. Finally, the BRBPN was trained in Neural Network toolbox of MATLAB6.0. The Preisach function values not at equal diversion points were calculated by the trained network. The actual displacement outputs and theoretical values corresponding to random voltages input were compared, and experimental results indicate that they agree with well.